首页|A Fractional-Order Ultra-Local Model-Based Adap-tive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

A Fractional-Order Ultra-Local Model-Based Adap-tive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

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This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspond-ingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC)is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is con-sidered as an unknown parameter.Then,the Nussbaum tech-nique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC)is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-sim-ulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

Adaptive controlinput deadzonemodel-free con-troln-DOF upper-limb exoskeletonneural network

Dingxin He、HaoPing Wang、Yang Tian、Yida Guo

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School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China

School of Automation,Nanjing University of Science and Technology,Nanjing 210094

Norinco Group Institute of Navigation and Control Technology,Beijing 100089,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaIntergovernmental International Science and Technology Innovation Cooperation Key Project of Chinese National Key R&D Progra

62173182617732122021YFE0102700

2024

10.1109/JAS.2023.123882

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(3)
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